Hybrid coarse-grid NCA plus implicit decoder produces arbitrary-resolution real-time outputs for morphogenesis and texture synthesis on grids and meshes while preserving self-organization.
Variational neural cellular au- tomata
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abstract
In nature, the process of cellular growth and differentiation has lead to an amazing diversity of organisms -- algae, starfish, giant sequoia, tardigrades, and orcas are all created by the same generative process. Inspired by the incredible diversity of this biological generative process, we propose a generative model, the Variational Neural Cellular Automata (VNCA), which is loosely inspired by the biological processes of cellular growth and differentiation. Unlike previous related works, the VNCA is a proper probabilistic generative model, and we evaluate it according to best practices. We find that the VNCA learns to reconstruct samples well and that despite its relatively few parameters and simple local-only communication, the VNCA can learn to generate a large variety of output from information encoded in a common vector format. While there is a significant gap to the current state-of-the-art in terms of generative modeling performance, we show that the VNCA can learn a purely self-organizing generative process of data. Additionally, we show that the VNCA can learn a distribution of stable attractors that can recover from significant damage.
representative citing papers
A learned local rule (Weight Transformer) iteratively self-organizes task-network weights from local graph neighborhoods and generalizes across unseen MLP, CNN, and ResNet architectures up to ~2M parameters.
citing papers explorer
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Neural Cellular Automata: From Cells to Pixels
Hybrid coarse-grid NCA plus implicit decoder produces arbitrary-resolution real-time outputs for morphogenesis and texture synthesis on grids and meshes while preserving self-organization.
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Architecture Generalization with MetaNCA
A learned local rule (Weight Transformer) iteratively self-organizes task-network weights from local graph neighborhoods and generalizes across unseen MLP, CNN, and ResNet architectures up to ~2M parameters.